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Basis-Adaptive Selection Algorithm in dr-package

Title
Basis-Adaptive Selection Algorithm in dr-package
Authors
Yoo, Jae Keun
Ewha Authors
유재근
SCOPUS Author ID
유재근scopus
Issue Date
2018
Journal Title
R JOURNAL
ISSN
2073-4859JCR Link
Citation
R JOURNAL vol. 10, no. 2, pp. 124 - 132
Publisher
R FOUNDATION STATISTICAL COMPUTING
Indexed
SCIE; SCOPUS WOS
Document Type
Article
Abstract
Sufficient dimension reduction (SDR) turns out to be a useful dimension reduction tool in high-dimensional regression analysis. Weisberg (2002) developed the dr-package to implement the four most popular SDR methods. However, the package does not provide any clear guidelines as to which method should be used given a data. Since the four methods may provide dramatically different dimension reduction results, the selection in the dr-package is problematic for statistical practitioners. In this paper, a basis-adaptive selection algorithm is developed in order to relieve this issue. The basic idea is to select an SDR method that provides the highest correlation between the basis estimates obtained by the four classical SDR methods. A real data example and numerical studies confirm the practical usefulness of the developed algorithm.
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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